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Establishment of a Laboratory Scale Set-Up with Controlled Temperature and High Humidity to Investigate Dry Matter Losses of Wood Chips from Poplar during Storage
The aim of this work was to improve the understanding of dry matter losses (DML) that occur in wood chips during the initial phase of storage in outdoor piles. For this purpose, a laboratory scale storage chamber was developed and investigated regarding its ability to recreate the conditions that chips undergo during the initial phase of outdoor storage. Three trials with poplar Max-4 (Populus maximowiczii Henry Populus nigra L.) chips were performed for 6–10 weeks in the storage chamber under controlled temperature and assisted humidity. Two different setups were investigated to maintain a high relative humidity (RH) inside the storage chamber; one using water containers, and one assisted with a humidifier. Moisture content (MC) and DML of the chips were measured at different storage times to evaluate their storage behaviour in the chamber. Additionally, microbiological analyses of the culturable fraction of saproxylic microbiota were performed, with a focus on mesophilic fungi, but discriminating also xerophilic fungi, and mesophilic bacteria, with focus on actinobacteria, in two trials, to gain a view on the poplar wood chip-inhabiting microorganisms as a function of storage conditions (moisture, temperature) and time. Results show that DML up to 8.8–13.7% occurred in the chips within 6–10 storage weeks. The maximum DML were reached in the trial using the humidifier, which seemed a suitable technique to keep a high RH in the testing chamber, and thus, to analyse the wood chips in conditions comparable to those in outdoor piles during the initial storage phase.
Supportive Business Environments to Develop Grass Bioeconomy in Europe
Grasslands cover almost half of the total European agricultural area and are the source of a wide range of public goods and services. Yet, their potential to produce innovative bio-based products, such as paper and plastic, remains widely untapped. We employ a multiple case study approach and implement the Business Environment Framework by Adamseged and Grundmann (2020) on eighteen alternative grass-based businesses to investigate the interdependencies between these successful business models and their business environments. The subsequent analysis reveals that the deployment of funds and policies to support alternative grass-based products remains low in most regions of Europe. Our findings highlight that aligned funding mechanisms that incorporate and promote the specific benefits generated by grass-producing and grass-processing businesses are key to overcoming the barriers related to the competition of bio-based products with the established fossil-fuels-based economic system. To make alternative grass-based markets more dynamic, increasing consumer awareness through adequate marketing is perceived as an important aspect. Capacity building and alignment efforts need to be strengthened and coordinated at local and higher levels to enable the replication and scale-up of novel grass-based businesses in Europe and beyond.
Spatial-Explicit Climate Change Vulnerability Assessments Based on Impact Chains. Findings from a Case Study in Burundi
Climate change vulnerability assessments are an essential instrument to identify regions most vulnerable to adverse impacts of climate change and to determine appropriate adaptation measures. Vulnerability assessments directly support countries in developing adaptation plans and in identifying possible measures to reduce adverse consequences of changing climate conditions. Against this background, this paper describes a vulnerability assessment using an integrated and participatory approach that builds on standardized working steps of previously developed ‘Vulnerability Sourcebook’ guidelines. The backbone of this approach is impact chains as a conceptual model of cause–effect relationships as well as a structured selection of indicators according to the three main components of vulnerability, namely exposure, sensitivity and adaptive capacity. We illustrate our approach by reporting the results of a vulnerability assessment conducted in Burundi focusing on climate change impacts on water and soil resources. Our work covers two analysis scales: a national assessment with the aim to identify climate change ‘hotspot regions’ through vulnerability mapping; and a local assessment aiming at identifying local-specific drivers of vulnerability and appropriate adaptation measures. Referring to this vulnerability assessment in Burundi, we discuss the potentials and constraints of the approach. We stress the need to involve stakeholders in every step of the assessment and to communicate limitations and uncertainties of the applied methods, indicators and maps in order to increase the comprehension of the approach and the acceptance of the results by different stakeholders. The study proved the practical usability of the approach at the national level by the selection of three particularly vulnerable areas. The results at a local scale supported the identification of adaption measures through intensive engagement of local rural populations.
Knowledge Transfer with Citizen Science: Luft-Leipzig Case Study
Community-based participatory research initiatives such as “hackAir”, “luftdaten.info”, “senseBox”, “CAPTOR”, “CurieuzeNeuzen Vlaanderen”, “communityAQ”, and “Healthy Air, Healthier Children” campaign among many others for mitigating short-lived climate pollutants (SLCPs) and improving air quality have reported progressive knowledge transfer results. These research initiatives provide the research community with the practical four-element state-of-the-art method for citizen science. For the preparation-, measurements-, data analysis-, and scientific support-elements that collectively present the novel knowledge transfer method, the Luft-Leipzig project results are presented. This research contributes to science by formulating a novel method for SLCP mitigation projects that employ citizen scientists. The Luft-Leipzig project results are presented to validate the four-element state-of-the-art method. The method is recommended for knowledge transfer purposes beyond the scope of mitigating short-lived climate pollutants (SLCPs) and improving air quality.
Effect of 1-Methyl Cyclopropane and Modified Atmosphere Packaging on the Storage of Okra (Abelmoschus esculentus L.) : Theory and Experiments
Okra possesses a short shelf-life which limits its marketability, thereby, the present study investigates the individual and combined effect of 1-methylcyclopropene (1-MCP) and modified atmosphere packaging (MAP) on the postharvest storage life of okra. The treated/ untreated okra samples were stored at ambient (i.e., 27 °C) and low (i.e., 7 °C) temperatures for eight and 20 days, respectively. Results revealed that the 1-MCP and/or MAP treatment successfully inhibited fruit softening, reduction in mucilage viscosity, and color degradation (hue angle, ∆E, and BI) in the product resulting in a longer period of shelf-life. However, MAP with or without 1-MCP was more effective to reduce weight loss in okra stored at both ambient and cold storage conditions. Additionally, ascorbic acid and total antioxidants were also retained in 1-MCP with MAP during cold storage. The 1-MCP in combination with MAP effectively suppressed respiration rate and ethylene production for four days and eight days at 27 °C and 7 °C temperature conditions, respectively. According to the results, relatively less chilling injury stress also resulted when 1-MCP combined with MAP. The combined treatment of okra pods with 1-MCP and MAP maintained the visual quality of the product in terms of overall acceptability for four days at 20 °C and 20 days at 7 °C.
Development of Biorefineries in the Bioeconomy: A Fuzzy-Set Qualitative Comparative Analysis among European Countries
This study aims to identify the configurational conditions that characterize the establish-ment of biorefineries in 20 European countries. After determining the conditions which support a bioeconomy transition, secondary data from national sources are used to represent their existing conditions within respective countries. Then, a fuzzy-set qualitative comparative analysis is em-ployed to compare and contrast the effect of varying combinations of the selected conditions on the development of biorefineries. The conditions chosen include coherent bioeconomy strategies, network intensity of regional bioclusters, intellectual capital, and natural resource availability. Our results reveal that the configuration of a coherent bioeconomy strategy, sizable public spending on R&D, abundant biomass supply, and a high level of network intensity is sufficient to explain the pro-nounced biorefineries development among some European countries. We recommend that countries with fragmented approaches review and redesign the policy and regulatory framework to create a holistic and consistent bioeconomy strategy, taking into account the configurations of conditions as an important prerequisite. In particular, factors such as the lack of best practice examples, the low level of public spending on research and development, the economic capacities for a skilled workforce in addition to the sustainable supply of raw materials should be addressed as focal points.
Anthropogenic Land Use Change and Adoption of Climate Smart Agriculture in Sub-Saharan Africa
Compelling evidence in Sub-Saharan Africa (SSA) shows that Climate-Smart Agriculture (CSA) has a positive impact on agricultural productivity. However, the uptake of CSA remains low, which is related to anthropogenic, or human-related, decisions about CSA and agricultural land use. This paper assesses households’ decisions to allocate agricultural land to CSA technologies across space and over time. We use the state-contingent theory, mixed methods, and mixed data sources. While agricultural land is increasing, forest land is decreasing across countries in SSA. The results show that household decisions to use CSA and the extent of agricultural land allocation to CSA remain low with a negative trend over time in SSA. Owned land and accessing land through rental markets are positively associated with allocating land to CSA technologies, particularly where land pressure is high. Regarding adaptation, experiencing rainfall shocks is significantly associated with anthropogenic land allocation to CSA technologies. The country policy assessment further supports the need to scale up CSA practices for adaptation, food security, and mitigation. Therefore, scaling up CSA in SSA will require that agriculture-related policies promote land tenure security and land markets while promoting climate-smart farming for food security, adaptation, and mitigation.
Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran
Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.
Investigating Solid and Liquid Desiccant Dehumidification Options for Room Air-Conditioning and Drying Applications
This study reports on the investigation of the performance of single and two-stage liquid and solid desiccant dehumidification systems and two-stage combined liquid and solid desiccant dehumidification systems with reference to humid climates. The research focus is on a dehumidification system capacity of 25 kW designed for room air conditioning application using the thermal models reported in the literature. RD-type silica gel and LiCl are used as solid and liquid desiccant materials, respectively. In this study, the application of proposed system for deep drying application is also explored. Condensation rate and moisture removal efficiency are chosen as performance parameters for room air conditioning application, whereas air outlet temperature is chosen as performance parameter for deep drying application. Further, for a given range of operating parameters, influences of air inlet humidity ratio, flow rate, and inlet temperature on performance parameters of the systems are investigated. In humid climatic conditions, it has been observed that a two-stage liquid desiccant dehumidification system is more effective for room air conditioning application, and two-stage solid desiccant dehumidification system is more suitable for deep drying application in the temperature range of 50 to 70 °C, while single-stage solid desiccant and two-stage combined liquid and solid desiccant dehumidification systems are more effective for low temperature, i.e., 30 to 50 °C deep drying application.
Out of the dark: Establishing a large-scale field experiment to assess the effects of artificial light at night on species and food webs
Artificial light at night (ALAN) is one of the most obvious hallmarks of human presence in an ecosystem. The rapidly increasing use of artificial light has fundamentally transformed nightscapes throughout most of the globe, although little is known about how ALAN impacts the biodiversity and food webs of illuminated ecosystems. We developed a large-scale experimental infrastructure to study the effects of ALAN on a light-naïve, natural riparian (i.e., terrestrial-aquatic) ecosystem. Twelve street lights (20 m apart) arranged in three rows parallel to an agricultural drainage ditch were installed on each of two sites located in a grassland ecosystem in northern Germany. A range of biotic, abiotic, and photometric data are collected regularly to study the short- and long-term effects of ALAN on behavior, species interactions, physiology, and species composition of communities. Here we describe the infrastructure setup and data collection methods, and characterize the study area including photometric measurements. None of the measured parameters differed significantly between sites in the period before illumination. Results of one short-term experiment, carried out with one site illuminated and the other acting as a control, demonstrate the attraction of ALAN by the immense and immediate increase of insect catches at the lit street lights. The experimental setup provides a unique platform for carrying out interdisciplinary research on sustainable lighting.